20210713 - Scooby Doo

# libraries
library(forcats)
library(lubridate)
library(plotly)
library(readr)
library(tidyverse)

# data
scoobydoo <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-07-13/scoobydoo.csv')

scooby doo

Introduction

This weeks Tidy Tuesday dataset comes from Kaggle by way of manual data aggregation from plummye.

Every Scooby-Doo episode and movie’s various variables.

Took ~1 year to watch every Scooby-Doo iteration and track every variable. Many values are subjective by nature of watching but I tried my hardest to keep the data collection consistent.

If you plan to use this data for anything school/entertainment related you are free to (credit is always welcome).

Exploratory Data Analysis

scoobydoo %>% head(5)

# tidy data 

# values
color_scheme1 <- c(
  "#228B22",# aka forest green (Best)
  "#98FB98", # aka pale green (Top 3)
  "#D3D3D3", # aka light grey (Other)
  "#FFB6C1", # aka light pink (Bottom 3)
  "#DC143C" # aka crimson (Worst)
  )

IMDb Time Series

Series

## get summary info for each season
series_info <- scoobydoo %>%
  mutate(
    year = year(date_aired),
    imdb = as.double(imdb),
    engagement = as.double(engagement)
  ) %>%
  group_by(series_name, network) %>%
  dplyr::summarise(
    series_start = min(date_aired),
    series_end = max(date_aired),
    n_episodes = n(),
    mean_imdb = mean(imdb, na.rm = TRUE),
    mean_engagement = mean(engagement, na.rm = TRUE)
  ) %>%
  ungroup() %>%
  filter(n_episodes > 1) %>% # filter out movie events
  arrange(series_start, series_end) %>%
  mutate(
    series_id = as.double(row_number())
  )

series_info <- series_info %>%
  mutate(
    ranking = case_when(
      series_id == head(arrange(series_info, desc(mean_imdb)), 1)$series_id ~ "Best Series",
      series_id %in% head(arrange(series_info, desc(mean_imdb)), 3)$series_id ~ "Top 3 Series",
      series_id == head(arrange(series_info, mean_imdb), 1)$series_id ~ "Worst Series",
      series_id %in% head(arrange(series_info, mean_imdb), 3)$series_id ~ "Bottom 3 Series",
      TRUE ~ "Other"
    ),
    ranking = factor(ranking, levels = c("Best Series", "Top 3 Series", "Other", "Bottom 3 Series", "Worst Series"))
  )

# plot series over time
series_info %>%
  plot_ly(
    type = 'bar',
    mode = 'markers',
    x = ~series_id,
    y = ~mean_imdb,
    color = ~ranking,
    colors = color_scheme1,
    text = ~paste0("<b>", series_name, "</b><br>",
                   "<i>Aired from ", series_start, " to ", series_end, " on ", network, "</i><br><br>",
                   "Mean IMDb Score: ", round(mean_imdb, 2), " (Number of Reviews: ", round(mean_engagement), ")<br>",
                   "Episodes: ", n_episodes, "<br>")
  ) %>%
  layout(
    title = 'IMDb Scores for Scooby Doo Series Over Time',
    xaxis = list(title = 'Sequential Series Number', showticklabels = FALSE),
    yaxis = list(title = 'Mean IMDb Score'),
    legend = list(orientation = 'h', y = -0.3),
    width = 800,
    height = 400
  )

Seasons

## get summary info for each season
season_info <- scoobydoo %>%
  mutate(
    year = year(date_aired),
    imdb = as.double(imdb),
    engagement = as.double(engagement)
  ) %>%
  group_by(series_name, network, season) %>%
  dplyr::summarise(
    season_start = min(date_aired),
    season_end = max(date_aired),
    n_episodes = n(),
    mean_imdb = mean(imdb, na.rm = TRUE),
    mean_engagement = mean(engagement, na.rm = TRUE)
  ) %>%
  ungroup() %>%
  filter(n_episodes > 1 & !(season %in% c("Movie", "Special"))) %>% # filter out movie events
  arrange(season_start, season_end) %>%
  mutate(
    season_id = as.double(row_number())
  )

season_info <- season_info %>%
  mutate(
    ranking = case_when(
      season_id == head(arrange(season_info, desc(mean_imdb)), 1)$season_id ~ "Best Season",
      season_id %in% head(arrange(season_info, desc(mean_imdb)), 3)$season_id ~ "Top 3 Season",
      season_id == head(arrange(season_info, mean_imdb), 1)$season_id ~ "Worst Season",
      season_id %in% head(arrange(season_info, mean_imdb), 3)$season_id ~ "Bottom 3 Season",
      TRUE ~ "Other"
    ),
    ranking = factor(ranking, levels = c("Best Season", "Top 3 Season", "Other", "Bottom 3 Season", "Worst Season"))
  )

The average Scooby Doo TV series has 2.0625 seasons. That’s so few!

# plot season over time
season_info %>%
  plot_ly(
    type = 'bar',
    mode = 'markers',
    x = ~season_id,
    y = ~mean_imdb,
    color = ~ranking,
    colors = color_scheme1,
    text = ~paste0("<b>", series_name, " - Season ", season, "</b><br>",
                   "<i>Aired from ", season_start, " to ", season_end, " on ", network, "</i><br><br>",
                   "Mean IMDb Score: ", round(mean_imdb, 2), " (Number of Reviews: ", round(mean_engagement), ")<br>",
                   "Episodes: ", n_episodes, "<br>")
  ) %>%
  layout(
    title = 'IMDb Scores for Scooby Doo Seasons Over Time',
    xaxis = list(title = 'Sequential Season Number', showticklabels = FALSE),
    yaxis = list(title = 'Mean IMDb Score'),
    legend = list(orientation = 'h', y = -0.3),
    width = 800,
    height = 400
  )

Episodes

scoobydoo %>%
  left_join(series_info, by = c("series_name", "network")) %>%
    filter(
    !(is.na(imdb)),
    imdb != "NULL",
    engagement != "NULL",
    !(season %in% c("Movie", "Special"))
  ) %>%
  plot_ly(
    type = 'scatter',
    mode = 'markers',
    x = ~index,
    y = ~imdb,
    color = ~ranking,
    colors = color_scheme1,
    text = ~paste0("<b>", title, "</b><br>",
                  "<i>Season ", season, " of Series ", series_name, "</i><br><br>",
                  "Aired ", date_aired, " on ", network, "<br>",
                  "IMDb Score ", imdb, " (Number of Reviews: ", engagement, ")")
  ) %>%
  layout(
    title = 'IMDb scores of Scooby Doo episodes over time',
    xaxis = list(title = 'Episode Index (according to Scoobypedia)'),
    yaxis = list(title = 'IMDb Score'),
    width = 800,
    height = 400,
    legend = list(orientation = 'h', y = -0.3)
  )

Monsters

Distribution of Gender

Distribution By Type

And subtype?

Or Species?

Who caught the monsters?

Who captured the monsters?

Who unmasked the monsters?

What percent of monsters got away?

Were not captured?

Which characters got the most scooby snacks?